Prompt Engineering & Optimization

Overview

What we deliver

We design, test, and refine prompts so your AI features produce accurate, consistent output across edge cases and model updates.

We turn rough prompts into production grade instructions that your AI systems can rely on. Our work starts with understanding the task, the audience, and the failure modes you want to avoid. We then build prompt templates with clear roles, examples, output formats, and guardrails, and we test them against a curated set of inputs that cover both common and edge cases. We compare versions, measure quality, and iterate until the prompts perform consistently. We also document the reasoning behind each prompt so your team can extend them with confidence. For teams running many prompts, we set up a prompt library with version control, change logs, and evaluation hooks. The result is fewer hallucinations, better structured responses, and AI features that behave the same way today, next week, and after the next model update.

Fit Check

Built for teams like yours

Who it's for

Product teams shipping AI features
Customer support automation projects
Content generation platforms
Internal tools using LLMs
Founders building AI MVPs

Pain points we solve

Inconsistent AI output
Hallucinations on edge cases
Prompts that break after model updates
No system for testing prompt changes
Unstructured responses that need parsing

What's included

Capabilities

Everything we cover in this engagement.

Prompt template design
Few shot example curation
Output schema definition
Guardrail and refusal handling
Prompt version control
A/B testing of variants
Edge case test sets
Prompt library setup

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discover

Map the task, users, and current failures.

02

Draft

Build prompt versions and test inputs.

03

Evaluate

Score outputs across the test set.

04

Refine

Iterate on weak areas and edge cases.

05

Hand off

Document and deliver the prompt library.

What you get

Deliverables & outcomes

What you get

Prompt templates
Test input library
Evaluation results
Prompt change log
Usage guide for engineers
Recommendations report

Outcomes you can expect

Higher response accuracy
Lower variance in output
Fewer support escalations
Faster prompt iteration cycles
Clear ownership of prompt assets

Timeline

2 to 6 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

PromptLayer, LangSmith, Promptfoo, OpenAI Playground, Anthropic Console

KPIs we track

Accuracy rate, hallucination rate, schema compliance, user satisfaction, iteration time

Client stories

What clients say

"

Our LCP was 4.8 seconds and Google was punishing us for it. They audited the build, dumped two plugins we did not need, moved hero images to a real CDN, and rewrote the critical CSS. LCP came down to 1.6 seconds within three weeks. Bounce rate on the pricing page dropped by a quarter without us touching the copy.

Sarah K.

"

We had been prototyping an AI quoting agent for nine months and could not get it past demo quality. They came in, scoped a real eval set, swapped our retrieval layer, and added guardrails for the edge cases that kept burning us. Went live in seven weeks. It now handles 41 percent of inbound quote requests without a human touching them.

Kyle A.

Proof